MATLAB: Predictions using NARX Network

Question

I am trying to build a network to do some long term predictions. My data is comprised of an INPUT and TARGET that spawns over 50 years (about 3500 points of data). At first I used the GUI to quickly get a network using default values. The network response seemed good but the ERROR Correlation and INPUT-ERROR cross correlation seem off( from what I understood from reading around here and the documentation, the peak should be at 0 lag). I tried adjusting the delays according to what I read in other question using the correlations but I don't understand how this works exactly. Where do I look to find the correct number of delays?

Another question I have is for long term predictions.Using the GUI I trained the network using around half of the data available, the network returned a very good approximation (with very little error between output and targets). Then in the next tab I used the TEST NETWORK and used the remaining points to see if it could predict the rest of the data. I would expect that at some point the error between output and target would grow but what I generally get is an excelent result where the output seems to be just a bit shifted under the target. (looks like the network learned everything when it is tested)

How can I correctly form predictions outside of the data I have?

I hope I was clear in my query.

Answer 1

You do not explain how you obtained your correlation functions.

Before you design a NARX, obtain the target-target autocorrelation function and the target-input cross-correlation function. Find the significant delays corresponding to peaks above the 95% confidence level of the cross-correlation of the target with random Gaussian noise

If you use nncorr, search my posts to avoid bugs in the code (e.g., the correct cross correlation function is not symmetric about zero lag).

greg nncorr

greg narxnet

Otherwise use ifft(conj(fft(x)).*fft(t)),or xcorr or crosscorr functions in other toolboxes .

The only thing that should have a peak at zero lag is the target autocorrelation function.

In order to predict future data, you need the preceding values of input and target to fill the delay buffers.

Hope this helps.

Thank you for formally accepting my answer

Greg